TimeDDPM: Time Series Augmentation Strategy for Industrial Soft Sensing

Soft sensor modeling for dynamic processes has become a trending topic and a pending challenge in industrial data analysis, especially in limited labeled data scenarios. Alternatively, data augmentation strategies provide a way to address the deficiency of samples. However, current time-series data augmentation methods do not consider the spatiotemporal dependencies among samples during the data generation procedure. To address the issue, a time-series denoising diffusion probabilistic model (TimeDDPM) is proposed to construct a soft sensor for finite time-series samples. First, the long short-term memory (LSTM) units and 1-D convolutional neural networks are implemented in the noise prediction network of TimeDDPM to mine both temporal and spatial properties of samples. Then, virtual samples are reconstructed step by step in the reverse process to enlarge the sample space of insufficient data. Finally, based on the augmented samples, the LSTM network is constructed as a base model to evaluate the quality of new training data. Two cases are employed to demonstrate the superiorities of the proposed method in comparison to several cutting-edge methods.